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Updated: Sep 28, 2025
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Glioma grading prediction using multiparametric magnetic resonance imaging-based radiomics combined with proton
Kun Lin1, Wangjiu Cidan2, Ying Qi1
1Department of Radiology, Shengjing Hospital of China Medical University, Heping District, Shenyang, Liaoning, China.
Purpose:
To evaluate the efficacy of three-dimensional (3D) segmentation-based radiomics analysis of multiparametric MRI combined with proton magnetic resonance spectroscopy (1 H-MRS) and diffusion tensor imaging (DTI) in glioma grading.
Method:
A total of 100 patients with histologically confirmed gliomas (grade II-IV) were examined using conventional MRI, 1 H-MRS, and DTI. Tumor segmentations of T1-weighted imaging (T1WI), contrast-enhanced T1WI (T1WI+C), T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) mapping, and fractional anisotropy (FA) mapping were performed. In total, 396 radiomics features were extracted and reduced using basic tests and least absolute shrinkage and selection operator (LASSO) regression. The selected features of each sequence were combined, and logistic regression with ten-fold cross-validation was applied to develop the grading model. Sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were compared. The model developed from the training set was applied to the test set to measure accuracy. One optimal grading quantitative parameter was selected for each 1 H-MRS and DTI analysis. A radiomics nomogram model including radiomics signature, quantitative parameters, and clinical features was developed.
Results:
T1WI+C exhibited the highest grading efficacy among single sequences (AUC, 0.92; sensitivity, 0.89; specificity, 0.85), but the efficacy of the combined model was higher (AUC, 0.97; sensitivity, 0.94; specificity, 0.91). The AUCs of all models exhibited high accuracy, and no significant differences were observed in AUCs between the training and test sets. The visualized nomogram was developed based on the combined radiomics signature and choline (Cho)/N-acetyl aspartate (NAA) from 1 H-MRS.
Conclusion:
Multiparametric MRI can be used to predict the pathological grading of high-grade gliomas (HGGs) and low-grade gliomas (LGGs) by combining radiomics features with quantitative parameters. The visualized nomogram may provide an intuitive assessment tool in clinical practice.
Clinical Trial Registration:
This trial was not registered, as it was a retrospective study and was approved by the local institutional review board.
